Single-Cell Analysis of NK Cell Cytotoxicity in Cancer Therapy Using Microfluidic Droplets
Bibliographic record
Abstract
Abstract Natural Killer (NK) cells are critical components of the immune system, uniquely capable of detecting and eliminating cancer cells without prior sensitization. Here, a droplet-based microfluidic platform is introduced that enables real-time monitoring and single-cell analysis of NK cell-mediated cytotoxicity against K562 cancer cells. Distinct NK cells are evaluated to quantify key metrics, including the percentage of cytotoxic NK cells, serial killing capacity, killing time per target, NK-target contact duration, and migration velocities. The results demonstrated that expanded NK cells (exNK) exhibited longer attachments, superior cytotoxic activity, serial killing, and rapid killing dynamics, whereas peripheral blood NK cells (pbNK), especially when they were exposed to ascites tumor microenvironment (TME) (pbNK-asc), displayed reduced cytotoxic abilities in all parameters. Interestingly, expanded NK cells exposed to ascites TME (exNK-asc) retained partial functionality, indicating that expansion provides resilience against suppressive factors. In addition, cell velocity analysis further revealed that the presence of a cancer cell increases the migration of NK cells. This single-cell analysis provides novel insights into NK-cancer cell interactions, offering a robust framework for enhancing the efficacy of future immunotherapy applications especially for optimizing off-the-shelf NK cell-based immunotherapies. Table of Contents A droplet-based microfluidic platform examines how Natural Killer (NK) cells target cancer cells at the single-cell level. By comparing peripheral blood and expanded NK cells under normal and tumor-like conditions, distinct differences in attachment, serial killing, killing time, and migration are revealed. These findings provide insights that could enhance tumor targeting, particularly in off-the-shelf NK cell therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".